發(fā)布時(shí)間：2018-04-22 16:37

  本文選題：近似熵 + 樣本熵�。� 參考：《計(jì)算機(jī)工程與設(shè)計(jì)》2014年03期

【摘要】：近似熵和樣本熵均是量化時(shí)間序列復(fù)雜性的重要指標(biāo),通過兩組數(shù)據(jù)集討論近似熵與樣本熵哪種更適合作為腦電特征。實(shí)驗(yàn)結(jié)果表明,樣本熵作為特征比近似熵能更恰當(dāng)反映情緒活動(dòng)存在的差異,差異電極主要集中在大腦前額;以樣本熵為分類特征識(shí)別嗜酒成癮者與正常人的平均準(zhǔn)確率為80.25%,高于近似熵為分類特征的74.25%,且樣本熵算法的計(jì)算時(shí)間比近似熵算法幾乎節(jié)約一半。相對(duì)于近似熵來(lái)說(shuō),樣本熵更適合作為腦電特征。
[Abstract]:Both approximate entropy and sample entropy are important indexes for quantifying the complexity of time series. This paper discusses which approximate entropy and sample entropy are more suitable for EEG characteristics through two groups of data sets. The experimental results show that the sample entropy as a feature can reflect the difference of emotional activity more appropriately than the approximate entropy, and the difference electrode is mainly concentrated on the forehead of brain. The average accuracy rate of identifying alcoholism addicts and normal people with sample entropy as classification feature is 80.25, which is higher than that with approximate entropy as classification feature 74.25, and the computing time of sample entropy algorithm is nearly half that of approximate entropy algorithm. Compared with approximate entropy, sample entropy is more suitable for EEG features.
【作者單位】： 太原理工大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;北京工業(yè)大學(xué)國(guó)際WIC研究院;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(61070077、61170136) 山西省自然科學(xué)基金項(xiàng)目(2010011020-2、2011011015-4) 北京市博士后工作經(jīng)費(fèi)基金項(xiàng)目(Q6002020201201)
【分類號(hào)】：TN911.7

【參考文獻(xiàn)】

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